Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging‐based brain template. Issue 6 (24th January 2022)

• Record Type: Journal Article
• Authors: Jin, Zhijia; Wang, Ying; Jokar, Mojtaba; Li, Yan; Cheng, Zenghui; Liu, Yu; Tang, Rongbiao; Shi, Xiaofeng; Zhang, Youmin; Min, Jihua; Liu, Fangtao; He, Naying; Yan, Fuhua; Haacke, Ewart Mark
• Is Part Of: Human brain mapping. Volume 43:Issue 6(2022)
• Page Start: 2011
• Page End: 2025
• Publication Date: 2022-01-24
• Subjects: automatic boundary detection -- brain template models -- midbrain nuclei -- neuromelanin imaging -- quantitative susceptibility mapping; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25770
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD : 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei. Abstract : Our goal was to use a magnetic resonance imaging (MRI) template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and neuromelanin (NM) contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence.